Managing the allocation of human resources and planning the optimal team capacity in the services sector is becoming increasingly complex because of the dynamic nature of allocating work schedules. Operations are often challenged with managing seemingly unmanageable work volumes with available staff. At times, the staff may feel overwhelmed with high work volumes, or underutilized during periods of low work volumes. Considerable amount of manual effort is spent on creating resource allocation schedules on a day to day basis.
Implementing human resource allocation schedule is of paramount importance as this allows organization to plan, assign and serve client needs in an optimal manner without exploitation or wastage of resources. Small scale organization usually make use of manual techniques or rough ratio-proportion methods to come up with a resource allocation or a capacity planning schedule. Certain other organizations use certain computational techniques by means of a heuristic approach and often equate human resource planning with material requirement planning techniques that do not quite scale up to the demands of a human resource requirement sector which is usually unpredictable and constantly changing in nature.
Human resource allocation, alternatively referred to as capacity planning is a method for using available human resources to assign and ensure completion of pre-defined tasks to meet service level agreements which is considered to be of utmost importance. Human resource allocation is typically performed by a Project manager exercising the Project manager's knowledge of the skills of the team members and their current tasks to determine optimal ways of allocating new or additional tasks within the team.
In one existing approach, a forecasting engine and a scheduling engine are utilized along with the static and dynamic human resource data of the organization. The static and dynamic human resource data is available in a manpower management system and this data is computed to arrive at approximate manpower hours using forecasted business volumes. The approach also provides for modifying, distributing and adjusting the generated manpower plan and schedule and is quite fundamental in nature.
Generally, it still continues to be quite a laborious and computationally intensive task to generate an optimal and dynamic resource allocation schedule which also satisfies the Service Level Agreements (SLAs) of varying nature such as fixed or constant SLAs and dynamic or variable SLAs. Therefore, current manual as well as automated techniques used in capacity planning have several inherent limitations pertaining to (a) the historical volume of data that can be processed (b) inability to perform real-time dynamic and an artificial intelligence embedded data analysis.